BAYESIAN SPATIAL ANALYSIS OF CHRONIC DISEASES IN ELDERLY CHINESE PEOPLE USING A STAR MODEL

Concept Analysis of Health-related Hardiness in Older People with Multiple Diseases Using a Hybrid Model

Background and purpose: Patients with hardiness personality could better control the psychological distress induced by multiple diseases. The present study aimed to clarify the concept of health related hardiness in older individuals with multiple diseases. Materials and methods: A hybrid method was used to analyze the concept of health related hardiness. This model consists of theoretical rev...

Quality of life in Chinese family caregivers for elderly people with chronic diseases

BACKGROUND Inadequate studies have been conducted in China to examine quality of life in family caregivers. Quality of life in family caregivers for elderly people with chronic diseases was evaluated, and the demographic and characteristic factors of both elderly people and their caregivers were explored. METHODS The 36-Item Short Form Health Survey (SF-36) was used to assess health-related q...

Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...

Concept analysis of empowerment in old people with chronic diseases using a hybrid model.

PURPOSE The purpose of this study was to clarify the meaning and the nature of empowerment concept in some Iranian old people suffering from chronic diseases. METHODS Concept analysis was undertaken according to the hybrid model, which consists of three phases: an initial theoretical phase, a fieldwork phase and a final analytical phase. After an extensive review of the literature in order to...

Bayesian Prediction in Spatial Data Analysis